Now showing 1 - 10 of 42
  • Publication
    Dynamic ant : introducing a new benchmark for genetic programming in dynamic environments
    (University College Dublin. School of Computer Science and Informatics, 2011-04-14) ; ; ; ;
    In this paper we present a new variant of the ant problem in the dynamic problem domain. This approach presents a functional dynamism to the problem landscape, where by the behaviour of the ant is driven by its ability to explore the search space being constrained. This restriction is designed in such a way as to ensure that no generalised solution to the problem is possible, thus providing a functional change in behaviour.
  • Publication
    Genetic Operators and Sequencing in the GAuGE System
    (IEEE, 2006-07-21) ;
    This paper investigates the effects of the mapping process employed by the GAuGE system on standard genetic operators. It is shown that the application of that mapping process transforms these operators into suitable sequencing searching tools. A practical application is analysed, and its results compared with a standard genetic algorithm, using the same operators. Results and analysis highlight the suitability of GAuGE and its operators, for this class of problems.
  • Publication
    On the use of Gene Dependency to Avoid Deceptive Traps
    (AAAI, 2002-07-13) ;
    THis paper presents a new approach to the field of genetic algorithms, basedon the indroduction of dependency between genes, as inspired by Grammatical Evolution. A system based on that approach, LINKGUAGE, is presented, and results reported show how the dependency between genes creates a tight linkage, guiding the system to success on hard deceptive linkage problems.
  • Publication
    A GAuGE Approach to Learning DFA from Noisy Samples
    This paper describes the adaptation of the GAuGE system to classify binary sequences generated by random DFA. Experiments were conducted, which, although not highly successful, illustrate the potential of applying GAuGE like systems to this problem domain.
  • Publication
    Grammar Defined Introns: An Investigation Into Grammars, Introns, and Bias in Grammatical Evolution
    (Morgan Kauffman, 2001-07-11) ; ;
    We describe an investigation into the design of different grammars on Grammatical Evolution. As part of this investigation we introduce introns using the grammar as a mechanism by which they may be incorporated into Grammatical Evolution. We establish that a bias exists towards certain production rules for each non-terminal in the grammar, and propose alternative mechanisms by which this bias may be altered either through the use of introns, or by changing the degeneracy of the genetic code. The benefits of introns for Grammatical Evolution are demonstrated experimentally.
  • Publication
    Evolving Interpolating Models of Net Ecosystem CO2 Exchange Using Grammatical Evolution
    Accurate measurements of Net Ecosystem Exchange of CO2 between atmosphere and biosphere are required in order to estimate annual carbon budgets. These are typically obtained with Eddy Covariance techniques. Unfortunately, these techniques are often both noisy and incomplete, due to data loss through equipment failure and routine maintenance, and require gap-filling techniques in order to provide accurate annual budgets. In this study, a grammar-based version of Genetic Programming is employed to generate interpolating models for flux data. The evolved models are robust, and their symbolic nature provides further understanding of the environmental variables involved.
      374Scopus© Citations 7
  • Publication
    Termination in Grammatical Evolution: Grammar Design, Wrapping, and Tails
    This paper explores the issues with mapping termination in Grammatical Evolution, and examines approaches that can be used to minimise them. It analyses the traditional approach of reusing the same genetic material, known as wrapping, and shows why this is inefficient with some grammars used in the literature. It suggests the appending of non-coding genetic material to genotype strings, at the start of the run, and shows the benefits of this approach: higher probability of creating terminated individuals, better or similar experimental performance, and a tendency to generate smaller solutions, when compared to the use of wrapping.
      438Scopus© Citations 16
  • Publication
    Dynamic Index Trading using a Gene Regulatory Network Model
    This paper presents a realistic study of applying a gene regulatory model to financial prediction. The combined adaptation of evolutionary and developmental processes used in the model highlight its suitability to dynamic domains, and the results obtained show the potential of this approach for real-world trading.
      399Scopus© Citations 1
  • Publication
    Genetic Algorithms using Grammatical Evolution
    (University of Limerick, 2006-09)
    This thesis proposes a new representation for genetic algorithms, based on the idea of a genotype to phenotype mapping process. It allows the explicit encoding of the position and value of all the variables composing a problem, therefore disassociating each variable from its genotypic location. The GAuGE system (Genetic Algorithms using Grammatical Evolution) is developed using this mapping process. In a manner similar to Grammatical Evolution, it ensures that there is no under- nor over-specification of phenotypic variables, therefore always producing syntactically valid solutions. The process is simple to implement and independent of the search engine used; in this work, a genetic algorithm is employed. The formal definition of the mapping process, used in this work, provides a base for analysis of the system, at different levels. The system is applied to a series of benchmark problems, defining its main features and potential problem domains. A thorough analysis of its main characteristics is then presented, including its interaction with genetic operators, the effects of degeneracy, and the evolution of representation. This in-depth analysis highlights the system’s aptitude for relative ordering problems, where not only the value of each variable is to be discovered, but also their correct permutation. Finally, the system is applied to the real-world problem of solving Sudoku puzzles, which are shown to be similar to instances of planning and scheduling problems, illustrating the class of problems for which GAuGE can prove to be a useful approach. The results obtained show a substantial improvement in performance, when compared to a standard genetic algorithm, and pave the way to new applications to problems exhibiting similar characteristics.
  • Publication
    Moving Towards Big Data Scalability with the Grammatical Evolution System
    The increasing presence of connected computing devices presents a formidable opportunity for the scalability of GP-like systems. In this work, we propose and partially implement a framework to deploy one such system, Grammatical Evolution, across a highly heterogeneous, asynchronous network of computing devices. We work towards a system combining the dynamic nature of such a network with the inherent adaptability of evolutionary systems. Early experiments are designed, using the open-source million song dataset.